Search engine optimization performance valuation

ABSTRACT

A way to promote a web site is via search engine optimization (SEO). Traditionally, SEO practitioners have charged a fixed fee for performing SEO services. An analyzer can determine a first probability that a search query for a campaign term will result in a referral from a search engine, determine second probabilities that are associated with a plurality of particular positions in a search engine results page, combine the search query volume information, the first probability, and the second probability for the position with a monetary value to generate an organic price, combine the organic prices for the plurality of positions to value the SEO services for the campaign term. The analyzer evaluates the effectiveness of SEO activities and can generate a volume-based value for SEO services. In addition, disclosed techniques can also be used to identify candidate terms for future SEO activities.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.12/555,224, filed Sep. 8, 2009, now U.S. Pat. No. 8,364,529, issued Jan.29, 2013, which claims the benefit under 35 U.S.C. §119(e) of U.S.Provisional Application No. 61/094,821, filed Sep. 5, 2008, each ofwhich is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

The invention relates to online marketing, and in particular, toautomation for monitoring and accounting in connection with searchengine optimization (SEO) marketing activities.

BACKGROUND Description of the Related Art

A search engine is a program that assists users to search forinformation on the Internet. When an Internet user enters a search queryinto an interface for a search engine, the search engine examines itsindex and provides a listing of matching documents. The usefulness of asearch engine depends on the relevance of the result set it generatesand shows to the web user. While there may be many documents thatinclude a particular word or phrase, some documents may be morerelevant, popular, or authoritative than others.

Some search engines provide search-related advertisements with regularsearch engine results in a search engine results page (SERP). A naturalresult set that should not be influenced by payment for listing is knownas an organic search result and occupies a space within the total resultset. A result set influenced by payment for listing is known as asponsored search result. FIG. 1 illustrates a conventional search engineresults page (SERP) which shows both organic and sponsored results.

One way to promote a web site is by purchasing advertising fromadvertising networks or publishers, such as on a pay per click (PPC)basis. Typically, advertising is selected based on an auction for theadvertising space. While an advertising network may offer a definedprice for advertising, such as an amount per click, it is more commonfor prices to be set through a bidding or auction arrangement.

Another way to direct traffic to a web site is via search engineoptimization (SEO). A goal of SEO is to improve the visibility of a website by making listings for the web site appear more frequently and moreprominently in the free (organic) portion of a search engine resultslisting. Many strategies exist for performing SEO.

While SEO can be performed by operators of a web site itself, SEOservices are typically performed by consultants or companies havingspecific SEO expertise. These SEO practitioners contract with a website's operators to improve the ranking of links to the web site in asearch engine's organic listing. For example, SEO practitioners can becompensated on a fixed fee basis, which can be supplemented with bonusestied to specific results, such as (1) how high links to the web site areplaced on the organic listing, (2) increases in Internet traffic (forexample, page hits), or (3) increases in sales as a result of better(higher) Internet traffic, or to combinations of the same.

SUMMARY

A way to promote a web site is via search engine optimization (SEO).Traditionally, SEO practitioners have charged a fixed fee for performingSEO services. An analyzer can determine a first probability that asearch query for a campaign term will result in a referral from a searchengine, determine second probabilities that are associated with aplurality of particular positions in a search engine results page,combine the search query volume information, the first probability, andthe second probability for the position with a monetary value togenerate an organic price, combine the organic prices for the pluralityof positions to value the SEO services for the campaign term. Theanalyzer evaluates the effectiveness of SEO activities and can generatea volume-based value for SEO services, which can be used as a referencefor providing SEO services.

In addition, disclosed techniques can also be used to identify candidateterms for SEO for future services. An analyzer computes rates ofreferrals for each term in a set of terms. The set of terms comprises,for example, terms that are used in search engine queries to access thesite. The analyzer identifies terms in the set that are underrepresentedin terms of rates of referral relative to one or more other terms in theset, and these underrepresented terms are identified as being suitablefor SEO.

BRIEF DESCRIPTION OF THE DRAWINGS

These drawings and the associated description herein are provided toillustrate specific embodiments of the invention and are not intended tobe limiting.

FIG. 1 illustrates an example of a search engine result page (SERP).

FIG. 2 illustrates inputs and outputs for an estimation model forvaluing organic referrals.

FIG. 3 is a flowchart generally illustrating a process for identifyingnew campaign terms (keywords).

FIG. 4 is a data flow diagram generally illustrating interaction withweb sites, with SEO service provides, and the like.

FIG. 5 illustrates an analyzer 502 can be used to implement anembodiment of the invention.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Although particular embodiments are described herein, other embodimentsof the invention, including embodiments that do not provide all of thebenefits and features set forth therein, will be apparent to those ofordinary skill in the art. In addition, while illustrated in the contextof web sites, the principles and advantages described herein areapplicable to other types of sites and interactive systems that areaccessible on a network.

Traditionally, SEO practitioners have charged a fixed fee for performingSEO services. While positions of listings (rankings) within an organicsearch result are not sold individually as PPC advertisements can be,these positions nonetheless have value. Techniques quantitativelymonitor and value these rankings, and can generate a volume-based valuefor SEO services, which can be used as a reference for providing SEOservices. These techniques can be implemented by software executed bycomputer hardware. All of the methods and processes described may beembodied in, and fully automated via, software code modules executed bycomputer hardware, such as one or more general purpose computers. Thecode modules may be stored on any type of physical computer-readablemedium or computer storage device. Some or all of the methods mayalternatively be embodied in specialized computer hardware. The resultsof the disclosed methods (e.g., generated data) may be storedpersistently on any type of physical computer data storage media orsystem. Instructions for the software can be stored in a tangible,computer-readable medium. Applicable computer systems include fileservers, web servers, database software, network monitoring equipment,and the like.

One embodiment is a technique that automatically determines valuationfor search engine optimization (SEO) services. Typically, particularphrases or keywords are selected as campaign terms for SEO by the website operator. A SEO practitioner is then hired to perform SEO for thecampaign terms. These campaign terms relate to query phrases used byInternet users when searching for information via a search engine tool.When, due to SEO activities, the campaign terms are identified as havingbeen used by Internet users as search queries in associated searchengine referrals, SEO activities can be valued.

To determine a valuation for SEO services, the illustrated embodiment ofthe invention uses “in-house,” which can be proprietary data related toSEO, and third-party data related to pay per click (PPC) advertising. Inone embodiment, the in-house or proprietary data comprises at leastpreviously collected search engine referral data and pricing data forthe same or related terms, and can be collected algorithmically ormanually. One embodiment of the invention permits SEO practitioners toestablish an intrinsic value for campaign terms, which can be used as areference for SEO optimization services. For example, averages of thesevalues for SEO services can be provided as values for similar terms;these values can vary according to performance metrics including:particular positions of links in listing, volumes of searches forcampaign terms, and long tail phrases for campaign terms. To determinevalue for campaign terms, one embodiment of the invention usesthird-party auction networks prices, for example, PPC pricing; however,these third-party prices are related to sponsored listings.

Valuation of Campaign Terms in Organic Searches

FIG. 2 illustrates inputs and outputs for a compensation model 202 forvaluing organic referrals. The compensation model 202 can usethird-party data 204, such as price information for PPC advertisements,in-house data 206, and volume data 208.

The third-party data 204 can include, for example, PPC price data andPPC volume data. The third-party data 204 can further be adjusted 210 toaccount for the difference in usage of selection of sponsored linksversus organic links within a SERF. For example, research indicates thatinformational searches are more common for organic listings than insponsored listings. Many web site operators are interested only inspecific transactional operations, such as sales, and thus, theseoperators will typically not value queries from organic search resultsequally as sponsored listings. Thus, sponsored listing prices should beadjusted to reflect these differences in value, such as: (1) differencesin the fractions of transactional operations; (2) differences in thevalue of transactional operations; (3) the position of the listing inthe organic space; and (4) the human perception that organic referralsare not as valuable as purchased (for example, PPC) referrals.

The in-house data 206 can include, for example, information regardinghistorical SEO service pricing for campaign terms, ranking performance,historical search activity volume, demographic data to identifypotentially similar or like user profiles, global search activityvolumes and behaviors, and referral volume behavior and patterns fromnon-search activity, such as hyperlink referrals wherein the hyperlinkhas anchor text (for example, <a href=http://no-site.com/>anchortext</a>) that is relevant to a particular term, and thus the activityfrom that link can be valued against outcomes from referrals. An outcomecan be any monetary or monetary equivalent value which may be assignedto an action (such as the value of a conversion) which occurred as aresult of a visitor who arrived via that referral. The volume data 208can include, for example, information related to the volume of searchesfor particular terms, the rates of selection of links in particularpositions, and the like.

One embodiment of the compensation model 202 will be described ingreater detail later in connection with Equation 1. The compensationmodel 202 can generate a reference for a price for SEO services for aparticular campaign term. For example, the value for SEO that results inthe topmost ranking organic search result (SE0-1) 212 can be estimatedby the compensation model 202. Similarly, other rankings such as thesecond highest ranking organic search result (SE0-2) 214 and anarbitrary N-th ranking SEO-N 216 can be computed. In an alternativeembodiment, rather than represent organic search result ranking, blocksSEO-x in FIG. 2 can represent different SEO practitioners, and thecompensation model 202 can be used to track the SEO performance of thevarious SEO practitioners. In an alternative embodiment, thecompensation model 202 ranks these different SEO practitioners based onthe relative performance of their SEO efforts, and generates a weightingfor the different SEO practitioners.

Thus, SEO-x can represent at least one of: (1) an organic search resultrank; (2) a particular SEO practitioner; or (3) a weight an SEOpractitioner has among all SEO practitioners (rank). Any or acombination of these three can be used as inputs for the generation ofan intrinsic value for campaign terms.

An organic price for placing a campaign term in a particular position(ranking) can depend on multiple factors, such as, but not limited to:(1) the number of search queries for the campaign term performed on agiven network; (2) the probability that the volume (number of searchqueries) will result into actual referrals; and (3) the probability thatsuch referrals take place from the desired position (ranking). When datais available, such as after SEO has been performed and data has beencollected, the foregoing probabilities can be replaced with fractionsbased on actual data. These factors can be used to calculate the numberof potential events. These events can include, but are not limited toreferrals, clicks, visits, conversions, in which an SEO campaign term isused by the user in the search query, and wherein the campaign termpresent within the specified position or ranges of positions on thesearch engine results page (SERP).

The calculated number of events can be multiplied by a price per event.A price for similar events can be obtained from in house price data,which typically includes previously-collected search engine referraldata and prices, whether collected algorithmically or via manual inputfor organic placement and/or from third-party data for placement on thepaid-space.

More than one in-house source and third-party source of price data canbe used as inputs. The in-house or proprietary data can change over timeand can also vary seasonally, and thus, data covering vary sets of timecan be selected. In one embodiment, the data is seasonally adjusted. Forexample, multiple sources of PPC data can be compiled from varioussearch engines. In one example, price data from each source can beweighted according to pre-defined rules. For example, a volume-weightedprice-average can be computed from multiple sources of PPC data, and theweighted average can be used to generate values for campaign terms.Pre-defined rules can also include weights that represent a perceivedperformance/usefulness of the various search engines from which PPC datais obtained. As is well known, some search engines produce more‘meaningful’ or useful results than others. While the perception ofmeaningfulness is qualitative, a weight can be assigned to each searchengine to adjust the PPC data of the search engines to account for thedifferences in usefulness. Pre-defined rules can also include weightsrepresenting the fact that organic referrals tend to be more informationseeking than referrals from paid listing. SEO values can be updated asdata is collected based on actual usage, and the collected data can bekept in the in-house data collection 206.

Equation 1 expresses an example of a mathematical model for estimatingthe value of placement of a link in a particular position Y in theorganic space of a search engine results page. In the illustratedembodiment, the mathematical model uses both in-house and third-partyprice data as inputs. In an alternative embodiment, either in-house orthird-party cost data suffices to estimate organic prices.OP(x,y)=(S _(x))(P _(s))(P _(Ry))(Σ(W)(IP)+Σ(M)(TP))  (Eq. 1)

In Equation 1, OP(x,y) represents the organic price for placement ofcampaign term x in position y (position y is a particular positionwithin a search engine results page; S_(x) represents the search queryvolume for campaign term x; P_(s) represents the fraction (when known,probability otherwise) of the search query phrase volume that convertsinto referrals; P_(Ry) represents the fraction (when known, probabilityotherwise) of referrals listed in position y; IP represents one or morein-house sources of price data, W represents one or more weightsdenoting the importance (for example, rank) of each one of the multitudeof in-house sources of price data; Σ(W) (IP) represents the sum of theone or more in-house sources of price data each source properlyweighted; TP represents one or more third-party sources of price data, Mrepresents one or more weights denoting the importance (for example,rank) of each of the third party sources of price data; Σ(M)(TP)represents the sum of the one or more third-party sources of price datawith each source properly weighted. In the illustrated embodiment, theformula expressed in Equation 1 is applied to a particular search engineto determine an organic price for placement on that search engine. In analternative embodiment, such as, for example, for use with a relativelynew search engine with little to no prior history, a combined, such asan average, organic price across multiple search engines can begenerated. The data considered in Equation 1 can also be restricted togeographical regions, to specified time periods, and the like. In oneembodiment, the weighted values Σ(W)(IP) and Σ(M)(TP) also vary byposition y. The results over various campaign terms x and positions ycan be summed.

Compensation models 202 can be combined with thresholds and/or limits toactivate/de-activate the compensation model 202. For example, thecompensation model 202 can be supplemented with a condition of notcharging for events until the total number of events has reached aminimum threshold. In another example, the compensation calculated bythe compensation model 202 can reach a cap when the compensation hasreached an upper limit.

In an alternative embodiment, pricing for events other than campaignterms are modeled, such as pricing for external hyperlinks (for pagesother than SERP) and conversions that are the result of visit due to SEOefforts. When these additional events are the result of SEO efforts, theevents should be assigned values so that SEO efforts are properlycompensated. In one example, a SEO practitioner and a site can specifythat particular pages are included in a SEO campaign, or that all pageswithin the site may be included. In addition, offsite pages (externalhyperlinks) can also be similarly identified. Typically, the arrangementbetween the SEO and the site is handled manually. Referring hyperlinksto the web site for which SEO is being performed are commonly recruitedfor at least two purposes: to expose the web site to additional visitorsvia the hyperlinks; and to increase a web site's importance (searchengine algorithms can use the prevalence of links to documents/web sitesto value the importance of a document or a web site). By procuring goodquality links, a SEO practitioner attracts both new customers and alsoincreases the exposure of the web site within the search engine resultpages. Some link referrals and some search engine referrals will resultin conversions; and if these referrals are the result of SEO effortstheir corresponding conversions should be assigned values so SEOactivities are compensated.

A variety of techniques can be used for assessing organic internetadvertising performance. For example, the processes described incommonly-owned U.S. patent application Ser. No. 12/143,387, filed Jun.20, 2008, titled “INTERNET MARKETING VERIFICATION TOOL,” the disclosureof which is hereby incorporated by reference herein can be used tocollect http referrers (referring hyperlinks or refer data) used toaccess a SEO optimized web site and to evaluate the collected refer dataagainst the campaign parameters for assessment of the Internetadvertising. See, for example, FIG. 4 and the accompanying descriptionof U.S. patent application Ser. No. 12/143,387. Campaign parametersinclude geographical origins of refer data, language preference of webuser/visitor, and landing pages for refer data.

Identification of New Campaign Terms (Keywords)

To drive even more volume to the pages of a web site, SEO can beperformed on additional terms. For example, there can be more than oneway to describe a topic, there can be more than one way to spell a word,and the like. Preferably, all relevant terms are optimized by SEO.However, oftentimes, there are terms that are unknown or otherwise notrecognized when SEO is being performed. This can happen frequently with,for example, new lingo, alternative spellings, or current events. Thesenew terms can be used as new campaign terms or keywords for SEO, furtherenhancing SEO efforts for the web site for which SEO is being performed.

In the context of online marketing, a web site operator would prefer tohave its search result listings in a relatively high position of a SERP,such as, for example, the first position of the first page of a SERP.However, some search terms are more competitive than others andachieving a desired position for a competitive search term may not bepractical for a particular web site. However, there may be other searchterms from which a web site receives hits (or can potentially receivehits) that are not as competitive as the most competitive search terms,and SEO can be performed for these other search terms to achieve highertraffic for the web site.

FIG. 3 illustrates a process that can be used to test and/or identifynew campaign terms (keywords). In this context, “new” relates to theidentification of terms for SEO efforts other than existing campaignterms, which, after SEO can generate statistically meaningful additionaltraffic to the web site. The process can be used to identify searchterms to be used for campaign terms, and can alternatively be used todetermine whether it is worth the expense to perform SEO for proposedcampaign terms.

The illustrated process can be modified in a variety of ways. Forexample, in another embodiment, various portions of the illustratedprocess can be combined, can be rearranged in an alternate sequence, canbe removed, or the like. At the start of the process, it is assumed thatdata for analysis has already been collected.

The process begins at a state 310 by defining the set of terms forinvestigation by the process. The set of terms are those terms that areat least worthy of investigation for possible SEO for the particular website, and thus, should be over inclusive. These terms can be words orphrases. These terms can be, for example, terms that were found to havebeen used by users as search queries to access web pages of the website, terms derived from analysis of web pages of the web site for whichSEO services are being considered, associated PPC terms, and any otherterm deemed worthy of investigation. The process then analyzes theseterms to determine whether SEO of these terms would be beneficial asdescribed below.

The process advances from the state 310 to a state 320 to collect searchquery data. For example, for each term in the set of terms, the processcan determine the corresponding search query volume, that is, how manytimes users used the particular term as a search query term. The countcan be limited to one or more selected search engines, to particularcountries or other geographic limitations, to particular time or dateranges, and the like. In one embodiment, the count is determined byretrieving the query volume information from a variety of search enginesand combining the retrieved information. In the tables that follow, thiscount corresponds to the variable NSQ.

An appropriate time or date range will vary depending upon thepopularity of a web site. For example, a high-volume web site maygenerate enough traffic for analysis in a relatively short period oftime. However, a relatively low-volume web site may need to have datacollected over a relatively lengthy time period, such as over 10 days,to collect enough data to be analyzed meaningfully. The process advancesfrom the state 320 to a state 330.

In the state 330, the process statistically analyzes the search querydata for each term of the set. In a simple example, the process cancompute the fraction of the total search query volume for each term inthe set, that is, the volume of search queries for each particular termof the set divided by the total volume of search queries in the set.This statistical analysis establishes a point of reference forreferrals. The process advances from the state 330 to a state 340.

In the state 340, the process counts the actual referrals to the website for each term in the set. In the tables that follow, this countcorresponds to the variable NRef. The prior constraints used in thestate 320, such as geographical constraints, time or date rangeconstraints, and the like, should again be applicable in the state 340.The process advances from the state 340 to a state 350.

In the state 350, the process analyzes the actual referrals. In oneexample, the process can compute the fraction of the total actualreferral volume for each term in the set, that is, the volume ofreferrals for each particular term of the set divided by the totalvolume of referrals in the set. This statistical analysis establishes apoint of reference for referrals. The process advances from the state350 to a decision block 360.

In the decision block 360, the process compares the statistical analysisof each of the terms in the set. In one example, the process comparesthe fraction of the total actual referral volume for a particularkeyword versus the fraction of the total search query volume (baselinereference) for said keyword. The comparison is used to determine whetherthe term is a good candidate for a campaign term SEO. For example, ifthe fraction of total actual referral volume is lower than a pre-definedamount of the fraction of the total search query volume (baseline) thensaid term is identified as underrepresented (for example, 30% versus50%).

In another example, the process compares the ranking of the fraction ofthe total actual referral volume for a particular keyword (term) versusthe ranking of the fraction of the total search query volume (baselinereference) for the particular keyword (term). For example, if theranking of the fraction of the total actual referral volume is lowerthan a predefined amount of the ranking of the fraction of the totalsearch query volume (baseline) then the keyword (term) is identified asunderrepresented (for example, rank 2 versus rank 5).

In another example, the process compares the ranking of the fraction ofthe total actual referral volume for all keywords versus the ranking ofthe fraction of the total search query volume (baseline reference) forall keywords. For example if the set of fractions of the total actualreferral volume for all keywords is statistically different from the setof fractions of the total search query volume for all keywords then adelta in fractions to the set of fractions of the total actual referralvolume is estimated so that the new fractions are similar (or identical)to the fractions of the total search query volume. For example, for eachterm in the set, the process computes a value for term volume TV as themaximum of the count of referrals NRef or the count of the search queryvolume NSQ. The maximum of the two is used to cap a later computed valuefor term ratio TR, as there can be discrepancies when the number ofreferrals from sources outside of search engines is relatively largesuch that there will be more referrals than search queries (sometimessearch query volumes are estimated from samples of the universe ofsearch queries; if samples are not representative then these may have nocorrelation with a TRue count (no sampling) of the universe of searchqueries) or when there are problem with data collection, or the like.The process then computes a value for the term ratio TR as the count ofreferrals NRef divided by the term volume TV. The process compares theterm ratio TR of each of the terms to a baseline reference to determinewhether the term is a good candidate for a campaign term SEO. In oneembodiment, the baseline reference is the best performing term in theset of terms, and terms below a predetermined percentage of the baselinereference value are identified for SEO.

The terms having a referral rate below a predefined amount of thereference baseline value are identified as underrepresented andpotential terms 370 for SEO. The predefined amount can vary widely basedon a variety of factors, such as on the overall value of SEO for the website. For example, underrepresented can be determined to be about 10% ormore below the reference baseline value, but other applicable valueswill be readily determined by one of ordinary skill in the art. Theterms that already have a similar referral rate to the baseline or arealready overrepresented are unlikely 380 to make good candidates forSEO.

In one embodiment, the process calculates a hypothetical amount ofvolume that could have been driven to the web site had selected terms inthe set been optimized as campaign terms for SEO. For example, theprocess can calculate the difference in rate between an expected rateand the actual rate, and multiply that difference in rate with thenumber of search queries NSQ for those terms. In addition, conversionrates can be used to determine an amount of revenue that would have beengenerated. These tools permit a web site operator and/or SEOpractitioner to determine whether it is worth the effort to perform SEOon selected terms.

Table 1 illustrates an example of computations using the process of FIG.3. Sample keywords labeled “A” to “D” are used. In this example, keyword“D” has the largest term ratio TR of 0.75 and is used as a baselinereference. The “Expected Traffic” computes the expected number ofreferrals on the site of interest if these terms were to be included inSEO. The “Potential Benefit” column is the difference between theexpected traffic and the actual number of referrals NRef, and the columnindicates the additional volume that the web site can potentiallyreceive per term if the terms are included in SEO efforts.

TABLE I Search Number Query Referrals Term Volume Term Ratio ExpectationPotential Term (NSQ) (NRef) (TV) (TR) Volume Benefit A 100 40 max(40,100) = 100 40/100 = 0.4 100 × 0.75 = 75 max(0, 75 − 40) = 35 B 15 10max(10, 15) = 15 10/15 = 0.66 15 × 0.75 = 11.25 max(0, 11.25 − 20) = 2.5C 30 20 max(20, 30) = 30 20/30 = 0.66 30 × 0.75 = 22.5 max(0, 22.5 − 20)= 2.5 D 40 30 max(30, 40) = 40 30/40 = 0.75 40 × 0.75 = 30 max(0, 30 −30) = 0

Table II illustrates that the percentage distribution of the “ExpectedVolume” should be about the same (identical in the example) to that ofthe “Search Query” volume.

TABLE II Search Expectation Query Distribution Volume Distribution idealTerm (NSQ) Search Query (referrals) traffic A 100 54% = 100/185 75 54% =75/138.75 B 15 8.1% = 15/185 11.25 8.1% = 11.25/138.75 C 30 16.2% =30/185 22.5 16.2% = 22.5/138.75 D 40 21.6% = 40/185 30 21.6% = 30/138.75Total 185 N/A 138.75 N/A

FIG. 4 is a data flow diagram generally illustrating interaction withweb sites, with SEO service provides, and the like. Campaign events 402are defined (referrals; hyperlinks; conversions; etc) to track. SEOresults are determined 404 by, for example, counting/trackingpre-defined events (for example, visits, actions, sales). Fraudulentevents are identified and filtered 406 (for example, high traffic but nosales). Events are verified 408 that they match pre-defined parameters(for example, sales from the United States). Values are assigned 410 toevents. Sources of revenue are identified 412 (for example, potentialreferrals/money/return-on-investment). Monetary compensation isdetermined 414 for SEO services provided or to be provided. A billing Iaccounting mechanism 416 provides billing to web site operator based onthe value of work delivered, and to the search marketer based onperformance of work delivered. A feedback loop mechanism 418 modifies acampaign based on sources of revenue (potential). After terms withrelatively good potential have been identified, these identified termscan be forwarded to SEO practitioners for optimization.

FIG. 5 illustrates an analyzer 502 can be used to implement anembodiment of the invention. For example, the analyzer 502 can beconfigured to implement the process described earlier in connection withFIG. 2 or the process described earlier in connection with FIG. 3. Thevarious systems illustrated in FIG. 5 can be implemented by computerhardware that executes software instructions.

FIG. 5 illustrates an the analyzer 502, a network 504, a web site 506for SEO, a search engine 508, a user computer 510, a monitoring system512, and a data repository 514. The network 504 can correspond to localarea networks and to wide area networks, such as the Internet. The website 506 represents a web site that is being optimized by SEOpractitioners or is considering using SEQ. The search engine 508provides search engine results pages (SERP) to the user computer 510.The monitoring system 512 and data repository 514 or data store can beused to collect http referrers (referring hyperlinks or refer data) usedto access a SEO-optimized web site and to evaluate the collected referdata against the campaign parameters for assessment of the Internetadvertising.

Various embodiments have been described above. Although described withreference to these specific embodiments, the descriptions are intendedto be illustrative and are not intended to be limiting. Variousmodifications and applications may occur to those skilled in the art.

What is claimed:
 1. A method of identifying terms suitable for search engine optimization (SEO) for a site, the method comprising: determining a volume of search queries for each term in a set of terms, wherein the set of terms comprises at least a plurality of terms used in search engine queries to access the site; counting referrals to the site for each term in the set of terms; computing a rate of referral for each term in the set of terms as a ratio of the counted referrals to the volume of search queries; computing an expectation volume for each term in the set of terms based on the highest rate of referral of the terms in the set of terms and the volume of search queries for each term in the set of terms; and identifying one or more terms in the set of terms that are underrepresented relative to one or more other terms in the set of terms based on the expectation volume for each term in the set of terms, wherein the one or more underrepresented terms are identified as being suitable for SEO; wherein the determining, the counting, and the computing are performed, at least in part, by one or more computing devices.
 2. The method of claim 1, wherein identifying the one or more terms in the set of terms that are underrepresented comprises: identifying that the one or more underrepresented terms have rates of referral outside of a selected statistical measure of a baseline reference, wherein the baseline reference is based on a highest rate of referral for a term in the set of terms.
 3. The method of claim 2, wherein the selected statistical measure is one standard deviation.
 4. The method of claim 1, further comprising: selecting terms for inclusion in the set of terms based on a specified time period or geographic region.
 5. The method of claim 1, further comprising: generating an estimate of additional referrals to the site due to additional referrals that would be generated if the SEO activities were performed for the one or more underrepresented terms; and generating at least one of a revenue estimate or a profit estimate based on the additional referrals.
 6. An apparatus for identifying terms suitable for search engine optimization (SEO) for a site, the apparatus comprising: a data store configured to store information related to search terms and referrals to the site; an analyzer comprising one or more computing devices, said analyzer configured to: compute a rate of referral for each term in a set of terms as a ratio of counted referrals to a volume of search queries, wherein the set of terms comprises at least a plurality of terms used in search engine queries to access the site, wherein the counted referrals comprise referrals to the site for each term in the set of terms, and wherein the volume of search queries comprises a volume of search queries for each term in the set of terms; compute an expectation volume for each term in the set of terms based on the highest rate of referral of the terms in the set of terms and the volume of search queries for each term in the set of terms; and identify one or more terms in the set of terms that are underrepresented relative to one or more other terms in the set of terms based on the expectation volume for each term in the set of terms, wherein the one or more underrepresented terms are identified as being suitable for SEO via statistical analysis.
 7. The apparatus of claim 6, wherein the analyzer is configured to identify the one or more terms in the set of terms that are underrepresented by identifying that the one or more underrepresented terms have rates of referral outside of a selected statistical measure of a baseline reference, wherein the baseline reference is based on the highest rate of referral for a term in the set of terms.
 8. The apparatus of claim 7, wherein the selected statistical measure is one standard deviation.
 9. The apparatus of claim 6, wherein the terms in the set of terms are selected for inclusion in the set of terms based on a specified time period or geographic region.
 10. The apparatus of claim 6, wherein the analyzer is further configured to: generate an estimate of additional referrals to the site due to additional referrals that would be generated if the SEO activities were performed for the one or more underrepresented terms; and generate at least one of a revenue estimate or a profit estimate based on the additional referrals.
 11. A non-transitory computer readable medium having instructions embodied thereon for identifying terms suitable for search engine optimization (SEO) for a site, wherein the instructions, in response to being executed by a computing device, cause the computing device to: determine a volume of search queries for each term in a set of terms, wherein the set of terms comprises at least a plurality of terms used in search engine queries to access the site; count referrals to the site for each term in the set of terms; compute a rate of referral for each term in the set of terms as a ratio of the counted referrals to the volume of search queries; compute an expectation volume for each term in the set of terms based on the highest rate of referral of the terms in the set of terms and the volume of search queries for each term in the set of terms; and identify one or more terms in the set of terms that are underrepresented relative to one or more other terms in the set of terms based on the expectation volume for each term in the set of terms, wherein the one or more underrepresented terms are identified as being suitable for SEO.
 12. The non-transitory computer readable medium of claim 11, wherein the instructions that cause the computing device to identify the one or more terms in the set of terms that are underrepresented comprise instructions that, in response to being executed by the computing device, cause the computing device to identify that the one or more underrepresented terms have rates of referral outside of a selected statistical measure of a baseline reference, wherein the baseline reference is based on a highest rate of referral for a term in the set of terms.
 13. The non-transitory computer readable medium of claim 12, wherein the selected statistical measure is one standard deviation.
 14. The non-transitory computer readable medium of claim 11, wherein the instructions, in response to being executed by the computing device, further cause the computing device to: select terms for inclusion in the set of terms based on a specified time period or geographic region.
 15. The non-transitory computer readable medium of claim 11, wherein the instructions, in response to being executed by the computing device, further cause the computing device to: generate an estimate of additional referrals to the site due to additional referrals that would be generated if the SEO activities were performed for the one or more underrepresented terms; and generate at least one of a revenue estimate or a profit estimate based on the additional referrals.
 16. An apparatus for identifying terms suitable for search engine optimization (SEO) for a site, the apparatus comprising: means for determining a volume of search queries for each term in a set of terms, wherein the set of terms comprises at least a plurality of terms used in search engine queries to access the site; means for counting referrals to the site for each term in the set of terms; means for computing a rate of referral for each term in the set of terms as a ratio of the counted referrals to the volume of search queries; means for computing an expectation volume for each term in the set of terms based on the highest rate of referral of the terms in the set of terms and the volume of search queries for each term in the set of terms; and means for identifying one or more terms in the set of terms that are underrepresented relative to one or more other terms in the set of terms based on the expectation volume for each term in the set of terms, wherein the one or more underrepresented terms are identified as being suitable for SEO; wherein the determining, the counting, and the computing are performed, at least in part, by one or more computing devices.
 17. The apparatus of claim 16, wherein the means for identifying the one or more terms in the set of terms is configured to identify that the one or more underrepresented terms have rates of referral outside of a selected statistical measure of a baseline reference, wherein the baseline reference is based on a highest rate of referral for a term in the set of terms.
 18. The apparatus of claim 17, wherein the selected statistical measure is one standard deviation.
 19. The apparatus of claim 16, further comprising: means for selecting terms for inclusion in the set of terms based on a specified time period or geographic region.
 20. The apparatus of claim 16, further comprising: means for generating an estimate of additional referrals to the site due to additional referrals that would be generated if the SEO activities were performed for the one or more underrepresented terms; and means for generating at least one of a revenue estimate or a profit estimate based on the additional referrals. 